System

Abstract

An object of a system according to an embodiment is to enable a purchaser to select clothing that suits the purchaser with confidence.SOLUTION: A system according to an embodiment includes a body type analysis unit, a preference analysis unit, a proposal unit, and a simulation unit. The body type analysis unit analyzes body type information of the purchaser. The preference analysis unit analyzes a preference of the purchaser. The proposal unit proposes optimal clothing based on the analysis results of the body type analysis unit and the preference analysis unit. The simulation unit performs a wearing simulation of the clothing item proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, when buying clothes online, it is difficult for the buyer to determine whether the clothes will suit them, which can lead to regret and anxiety.

[0005] The system according to the embodiment aims to enable a purchaser to confidently select clothes that suit them. [Means for solving the problem]

[0006] The system according to the embodiment includes a body type analysis unit, a preference analysis unit, a suggestion unit, and a simulation unit. The body type analysis unit analyzes the body type information of the purchaser. The preference analysis unit analyzes the preferences of the purchaser. The suggestion unit suggests optimal clothing based on the analysis results of the body type analysis unit and the preference analysis unit. The simulation unit performs a wearing simulation of the clothing suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a purchaser to confidently select clothes that suit them. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The application according to the embodiment of the present invention is a system that analyzes the body type and preferences of a purchaser, suggests optimal clothing, and performs a simulation of wearing the clothing, thereby enabling the purchaser to purchase clothing with confidence and eliminating regrets and anxieties.

[0029] The application according to the embodiment includes a body type analysis unit, a preference analysis unit, a suggestion unit, and a simulation unit. The body type analysis unit analyzes the body type information of a purchaser. For example, the body type analysis unit analyzes the body type based on information such as height, weight, and body fat percentage entered by the purchaser. The body type analysis unit can also perform a detailed body type analysis using 3D scan data of the purchaser. The body type analysis unit can also analyze the body type information of the purchaser in combination with past purchase history and social media posts. The preference analysis unit analyzes the preferences of the purchaser. For example, the preference analysis unit performs an analysis based on the color, style, and brand preferences entered by the purchaser. The preference analysis unit can also estimate preferences by analyzing the purchaser's past purchase history and social media posts. The preference analysis unit can also collect and comprehensively analyze feedback from multiple people to incorporate the opinions of family and friends. The suggestion unit suggests optimal clothing based on the analysis results of the body type analysis unit and the preference analysis unit. For example, the suggestion unit searches the ZOZOTOWN database for clothing that matches the purchaser's body type and preferences and suggests them. The suggestion unit can also suggest optimal clothing by taking into account the purchaser's lifestyle and activities. The suggestion unit can also analyze the purchaser's personal colors, such as skin color, hair color, and eye color, to suggest clothing in the most suitable shade. The simulation unit performs a wearing simulation of the clothing suggested by the suggestion unit. For example, the simulation unit may use a 3D model to simulate the impression the purchaser will have when wearing the suggested clothing, generating visually confirmable images and videos. The simulation unit can also reflect the purchaser's movements and posture in real time to simulate a more realistic wearing experience. The simulation unit can also provide a function that allows the purchaser to set different lighting conditions and backgrounds to check how the clothing will feel in various situations. This allows the application according to the embodiment to enable the purchaser to purchase clothing with confidence and eliminate regrets and anxieties. For example, the purchaser can check the suggested clothing in a simulation before trying it on, thereby improving post-purchase satisfaction. Furthermore, the purchaser can make a more appropriate choice by referring to other users' ratings and comments.

[0030] The body type analysis unit analyzes the purchase history of the purchaser or the content of posts on social media to estimate preferences with higher accuracy. The body type analysis unit, for example, analyzes the purchase history of the purchaser to identify frequently purchased items and brands. For example, the body type analysis unit estimates preference trends based on the styles and colors of items the purchaser has previously purchased. The body type analysis unit also analyzes the content of posts on social media to understand what kind of fashion the purchaser is interested in. For example, the body type analysis unit estimates the purchaser's preferred style based on posts that the purchaser has "liked" or commented on. The body type analysis unit also integrates the purchase history of the purchaser with social media data to estimate preferences with higher accuracy. For example, the body type analysis unit analyzes a combination of items the purchaser has previously purchased and their social media activity. In this way, by analyzing the purchaser's purchase history and the content of posts on social media, it is possible to estimate preferences with higher accuracy.

[0031] The body shape analysis unit can obtain the purchaser's body shape information using 3D scanning technology and perform a detailed body shape analysis. For example, the purchaser uses a smartphone camera to 3D scan their body shape and upload the data to the application. For example, by taking a full-body rotation image, detailed body shape data can be obtained. The body shape analysis unit also uses 3D scanning technology to accurately measure the purchaser's body shape, and the generative AI uses that data to suggest optimal clothing. For example, it analyzes shoulder width and waist measurements in detail. The body shape analysis unit also saves the purchaser's body shape data as a 3D model, making it available for future purchases. For example, it can track changes in body shape based on past scan data. This allows the use of 3D scanning technology to perform a detailed analysis of the purchaser's body shape information.

[0032] The preference analysis unit can collect feedback from multiple people and analyze it comprehensively in order to incorporate the opinions of family or friends. For example, the preference analysis unit provides a function that allows a purchaser to ask family and friends for their opinions on clothes they plan to purchase. For example, it makes it easy to collect feedback within the application. The preference analysis unit also analyzes the feedback from family and friends and comprehensively evaluates the purchaser's preference trends. For example, it integrates the opinions of multiple people to make optimal suggestions. The preference analysis unit also analyzes the emotional reactions of purchasers when selecting clothes with family and friends and estimates preference trends based on that data. For example, it places emphasis on opinions in which the purchaser expresses positive emotions. This makes it possible to analyze preferences more comprehensively by incorporating the opinions of family and friends.

[0033] The preference analysis unit can add a function that dynamically changes according to the season or event, making it possible to make optimal suggestions at any given time. The preference analysis unit, for example, suggests the most suitable clothing for the season or event based on the purchaser's body type and preference information. For example, it suggests clothing made of cool materials in the summer and clothing made of warm materials in the winter. Furthermore, when a purchaser is attending a specific event, the preference analysis unit suggests clothing suitable for that event. For example, it suggests formal clothing suitable for weddings and parties. Furthermore, the preference analysis unit builds a system that dynamically updates the purchaser's preference trends according to the season or event. For example, it can handle cases where preferred styles change with the seasons. This makes it possible to make optimal suggestions according to the season or event.

[0034] The suggestion unit can suggest optimal clothing by taking into consideration the lifestyle or activities of the purchaser. The suggestion unit, for example, suggests optimal clothing based on the lifestyle and activities of the purchaser. For example, it suggests sportswear that is easy to move in to a purchaser with an active lifestyle. The suggestion unit also analyzes the activities that the purchaser engages in daily and suggests clothing suitable for those activities. For example, it suggests business casual clothing to a purchaser who does a lot of office work. The suggestion unit also suggests optimal clothing according to the season and weather based on the lifestyle and activities of the purchaser. For example, it suggests waterproof clothing to a purchaser who does a lot of outdoor activities. This makes it possible to suggest optimal clothing according to the lifestyle and activities of the purchaser.

[0035] The suggestion unit can analyze the purchaser's personal color, such as skin color, hair color, or eye color, and suggest clothing in a color that suits them best. For example, the suggestion unit analyzes the purchaser's skin color, hair color, or eye color and suggests clothing in a color that suits them best. For example, it suggests clothing in a tone that matches their skin color. The suggestion unit also performs a personal color diagnosis and suggests clothing in a color that suits the purchaser best. For example, it classifies suggestions into spring type, summer type, autumn type, and winter type. The suggestion unit also suggests clothing in a color that suits the season or event based on the purchaser's personal color. For example, it suggests clothing in a color that suits a specific season. This makes it possible to suggest optimal clothing based on the purchaser's personal color.

[0036] The suggestion unit can take into consideration the hobbies or interests of the purchaser and suggest clothing with related designs and brands. For example, the suggestion unit suggests clothing with related designs and brands based on the hobbies and interests of the purchaser. For example, for a purchaser whose hobby is the outdoors, it suggests clothing with outdoor brands. The suggestion unit also analyzes themes and trends in which the purchaser is interested and suggests clothing with designs and brands related to those themes. For example, for a purchaser who is interested in eco-friendly themes, it suggests clothing with sustainable brands. The suggestion unit also suggests clothing with optimal designs and brands according to the season or event based on the hobbies and interests of the purchaser. For example, for a purchaser who is attending a music festival, it suggests clothing with designs and brands suitable for festivals. This makes it possible to suggest optimal clothing based on the hobbies and interests of the purchaser.

[0037] The suggestion unit can also consider the preferences of the purchaser's friends or family and suggest coordination that can be enjoyed by multiple people. The suggestion unit, for example, suggests coordination that can be enjoyed by multiple people based on the preferences of the purchaser's friends and family. For example, it suggests clothes with matching designs and colors for the whole family. Furthermore, when the purchaser attends an event with friends or family, the suggestion unit suggests coordination that is suitable for the event. For example, it suggests coordination that is suitable for a party to be attended with friends. Furthermore, the suggestion unit suggests optimal coordination according to the season or event based on the preferences of the purchaser's friends and family. For example, it suggests coordination that is suitable for a family trip. This makes it possible to suggest coordination that takes into account the preferences of the purchaser's friends and family.

[0038] The simulation unit reflects the purchaser's movements or posture in real time, enabling a more realistic simulation of the wearing sensation. For example, the simulation unit captures the purchaser's movements and posture in real time and simulates the wearing sensation of the clothing based on that data. For example, it reflects the purchaser's walking and sitting movements. The simulation unit also allows the purchaser to capture their own movements using a smartphone camera and upload the data to an application. For example, detailed movement data is obtained by taking a picture of the entire body while rotating. The simulation unit also builds a system that updates the clothing simulation in real time based on the purchaser's movements and posture. For example, the clothing simulation changes every time the purchaser moves. This allows a more realistic simulation of the wearing sensation to be achieved by reflecting the purchaser's movements and posture in real time.

[0039] The simulation unit can add a function that allows a purchaser to set different lighting conditions or backgrounds and check how the clothing will feel when worn in various situations. For example, the simulation unit provides a function that allows a purchaser to select different lighting conditions or backgrounds and simulates the clothing according to the settings. For example, the purchaser can select indoor or outdoor, daytime or nighttime situations. The simulation unit also simulates how the color and texture of the clothing will look by changing the lighting conditions and background. For example, the simulation unit compares how the clothing will look under different lighting conditions. The simulation unit also allows a purchaser to set a situation that matches a specific event or location and simulate how the clothing will feel when worn in that situation. For example, the purchaser can set a background of a beach or a party. This allows the purchaser to check how the clothing will feel when worn in various situations by setting different lighting conditions and backgrounds.

[0040] The simulation unit can also feature avatars of the purchaser's friends or family members to simulate outfits for multiple people. The simulation unit provides, for example, a function for creating avatars of the purchaser's friends and family members and having them appear in the simulation. For example, the simulation unit simulates outfits of matching designs and colors for the whole family. When the purchaser attends an event with friends or family members, the simulation unit simulates outfits suitable for the event. For example, the simulation unit simulates outfits suitable for a party attended with friends. The simulation unit also simulates optimal outfits according to the season or event, based on the avatars of the purchaser's friends and family members. For example, the simulation unit simulates outfits suitable for a family trip. This makes it possible to simulate outfits for multiple people using avatars of the purchaser's friends and family members.

[0041] The simulation unit can add a function to display other users' ratings or comments on the clothes selected by the purchaser in real time. The simulation unit provides, for example, a function to display other users' ratings and comments on the clothes selected by the purchaser in real time. For example, it displays the "likes" and comments that other users have made. The simulation unit also displays an overall rating of the clothes selected by the purchaser based on other users' ratings and comments. For example, it prioritizes displaying items with many positive ratings. The simulation unit also analyzes other users' feedback on the clothes selected by the purchaser and makes optimal suggestions based on that data. For example, it suggests items that other users have highly rated. In this way, by displaying other users' ratings and comments in real time, the simulation unit supports the purchaser's selection.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The suggestion unit can take into consideration the hobbies and interests of the purchaser and suggest clothing with related designs and brands. For example, for a purchaser whose hobby is the outdoors, it will suggest clothing from outdoor brands. The suggestion unit can also analyze themes and trends that the purchaser is interested in and suggest clothing with designs and brands related to those themes. For example, for a purchaser who is interested in eco-friendly themes, it will suggest clothing from sustainable brands. The suggestion unit can also suggest clothing with optimal designs and brands according to the season or event based on the purchaser's hobbies and interests. For example, for a purchaser attending a music festival, it will suggest clothing with designs and brands suited to festivals. This makes it possible to suggest optimal clothing based on the purchaser's hobbies and interests.

[0044] The simulation unit reflects the purchaser's movements and posture in real time, enabling a more realistic simulation of how the garment will feel when worn. For example, it reflects the purchaser's walking and sitting movements. The simulation unit also allows the purchaser to capture their own movements using their smartphone camera and upload the data to the application. For example, detailed movement data can be obtained by taking a picture of the entire body while rotating. The simulation unit also builds a system that updates the clothing simulation in real time based on the purchaser's movements and posture. For example, the clothing simulation changes every time the purchaser moves. This allows a more realistic simulation of how the garment will feel when worn by reflecting the purchaser's movements and posture in real time.

[0045] The suggestion unit can suggest optimal clothing by taking into consideration the lifestyle and activities of the purchaser. For example, it will suggest sportswear that is easy to move in to a purchaser with an active lifestyle. The suggestion unit also analyzes the purchaser's daily activities and suggests clothing suitable for those activities. For example, it will suggest business casual clothing to a purchaser who does a lot of office work. The suggestion unit also suggests optimal clothing according to the season and weather based on the purchaser's lifestyle and activities. For example, it will suggest waterproof clothing to a purchaser who does a lot of outdoor activities. This makes it possible to suggest optimal clothing according to the purchaser's lifestyle and activities.

[0046] The simulation unit can add a function that allows a purchaser to set different lighting conditions and backgrounds and check how the clothing will feel when worn in various situations. For example, a function that allows a purchaser to select different lighting conditions and backgrounds is provided, and a simulation of the clothing is performed according to the settings. For example, the purchaser can select indoor or outdoor, daytime or nighttime situations. The simulation unit can also simulate how the color and texture of the clothing will look by changing the lighting conditions and background. For example, the simulation unit can compare how the clothing will look under different lighting conditions. The simulation unit can also set a situation that matches a specific event or location, and simulate how the clothing will feel when worn in that situation. For example, the purchaser can set a background of a beach or a party. This allows the purchaser to check how the clothing will feel when worn in various situations by setting different lighting conditions and backgrounds.

[0047] The suggestion unit can analyze the personal colors of the purchaser, such as skin color, hair color, and eye color, and suggest clothing in the most suitable shade. For example, it can analyze the skin color, hair color, and eye color of the purchaser and suggest clothing in the most suitable shade. For example, it can suggest clothing in a tone that matches the skin color. The suggestion unit also performs a personal color diagnosis and suggests clothing in the most suitable shade for the purchaser. For example, it can classify and suggest clothing into spring type, summer type, autumn type, and winter type. The suggestion unit can also suggest clothing in the most suitable shade for a season or event based on the purchaser's personal color. For example, it can suggest clothing in a shade that suits a specific season. This makes it possible to suggest the most suitable clothing based on the purchaser's personal color.

[0048] The suggestion unit can also consider the preferences of the purchaser's friends and family to suggest outfits that can be enjoyed by multiple people. For example, it suggests outfits that can be enjoyed by multiple people based on the preferences of the purchaser's friends and family. For example, it suggests clothes with matching designs and colors for the whole family. Furthermore, when the purchaser attends an event with friends or family, the suggestion unit suggests outfits that are suitable for the event. For example, it suggests outfits that are suitable for a party to be attended with friends. Furthermore, the suggestion unit suggests optimal outfits according to the season or event based on the preferences of the purchaser's friends and family. For example, it suggests outfits that are suitable for a family trip. This makes it possible to suggest outfits that take into account the preferences of the purchaser's friends and family.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The body type analysis unit analyzes the buyer's body type information. For example, it analyzes body type based on information entered by the buyer, such as height, weight, and body fat percentage. It can also perform a detailed body type analysis using the buyer's 3D scan data. It can also analyze the buyer's body type information in combination with past purchase history and social media posts. Step 2: The preference analysis unit analyzes the buyer's preferences. For example, it analyzes based on the buyer's input preferences for color, style, and brand. It can also infer preferences by analyzing the buyer's past purchase history and social media posts. It can also collect feedback from multiple people to incorporate the opinions of family and friends and perform a comprehensive analysis. Step 3: The suggestion unit suggests the most suitable clothing based on the analysis results of the body type analysis unit and preference analysis unit. For example, it searches a database for clothing that suits the purchaser's body type and preferences and suggests the best clothing. It can also suggest the most suitable clothing taking into account the purchaser's lifestyle and activities. It can also analyze the purchaser's personal colors, such as skin color, hair color, and eye color, and suggest clothing in the most suitable shades. Step 4: The simulation unit simulates wearing the clothing proposed by the proposal unit. For example, it uses a 3D model to simulate the impression a purchaser will have when wearing the proposed clothing, generating images and videos that can be visually confirmed. It can also reflect the purchaser's movements and posture in real time to simulate a more realistic wearing experience. It can also provide a function that allows users to set different lighting conditions and backgrounds to check how the clothing will feel in various situations.

[0051] (Example 2) The application according to the embodiment of the present invention is a system that analyzes the body type and preferences of a purchaser, suggests optimal clothing, and performs a simulation of wearing the clothing, thereby enabling the purchaser to purchase clothing with confidence and eliminating regrets and anxieties.

[0052] The application according to the embodiment includes a body type analysis unit, a preference analysis unit, a suggestion unit, and a simulation unit. The body type analysis unit analyzes the body type information of a purchaser. For example, the body type analysis unit analyzes the body type based on information such as height, weight, and body fat percentage entered by the purchaser. The body type analysis unit can also perform a detailed body type analysis using 3D scan data of the purchaser. The body type analysis unit can also analyze the body type information of the purchaser in combination with past purchase history and social media posts. The preference analysis unit analyzes the preferences of the purchaser. For example, the preference analysis unit performs an analysis based on the color, style, and brand preferences entered by the purchaser. The preference analysis unit can also estimate preferences by analyzing the purchaser's past purchase history and social media posts. The preference analysis unit can also collect and comprehensively analyze feedback from multiple people to incorporate the opinions of family and friends. The suggestion unit suggests optimal clothing based on the analysis results of the body type analysis unit and the preference analysis unit. For example, the suggestion unit searches the ZOZOTOWN database for clothing that matches the purchaser's body type and preferences and suggests them. The suggestion unit can also suggest optimal clothing by taking into account the purchaser's lifestyle and activities. The suggestion unit can also analyze the purchaser's personal colors, such as skin color, hair color, and eye color, to suggest clothing in the most suitable shade. The simulation unit performs a wearing simulation of the clothing suggested by the suggestion unit. For example, the simulation unit may use a 3D model to simulate the impression the purchaser will have when wearing the suggested clothing, generating visually confirmable images and videos. The simulation unit can also reflect the purchaser's movements and posture in real time to simulate a more realistic wearing experience. The simulation unit can also provide a function that allows the purchaser to set different lighting conditions and backgrounds to check how the clothing will feel in various situations. This allows the application according to the embodiment to enable the purchaser to purchase clothing with confidence and eliminate regrets and anxieties. For example, the purchaser can check the suggested clothing in a simulation before trying it on, thereby improving post-purchase satisfaction. Furthermore, the purchaser can make a more appropriate choice by referring to other users' ratings and comments.

[0053] The body type analysis unit analyzes the purchase history of the purchaser or the content of posts on social media to estimate preferences with higher accuracy. The body type analysis unit, for example, analyzes the purchase history of the purchaser to identify frequently purchased items and brands. For example, the body type analysis unit estimates preference trends based on the styles and colors of items the purchaser has previously purchased. The body type analysis unit also analyzes the content of posts on social media to understand what kind of fashion the purchaser is interested in. For example, the body type analysis unit estimates the purchaser's preferred style based on posts that the purchaser has "liked" or commented on. The body type analysis unit also integrates the purchase history of the purchaser with social media data to estimate preferences with higher accuracy. For example, the body type analysis unit analyzes a combination of items the purchaser has previously purchased and their social media activity. In this way, by analyzing the purchaser's purchase history and the content of posts on social media, it is possible to estimate preferences with higher accuracy.

[0054] The body shape analysis unit can obtain the purchaser's body shape information using 3D scanning technology and perform a detailed body shape analysis. For example, the purchaser uses a smartphone camera to 3D scan their body shape and upload the data to the application. For example, by taking a full-body rotation image, detailed body shape data can be obtained. The body shape analysis unit also uses 3D scanning technology to accurately measure the purchaser's body shape, and the generative AI uses that data to suggest optimal clothing. For example, it analyzes shoulder width and waist measurements in detail. The body shape analysis unit also saves the purchaser's body shape data as a 3D model, making it available for future purchases. For example, it can track changes in body shape based on past scan data. This allows the use of 3D scanning technology to perform a detailed analysis of the purchaser's body shape information.

[0055] The body type analysis unit can use the emotion estimation function to analyze the emotional responses of a purchaser to clothes purchased in the past and estimate preference trends. The body type analysis unit, for example, analyzes the emotional responses of a purchaser to clothes purchased in the past and estimates preference trends based on the data. For example, it preferentially suggests items for which the purchaser expressed positive emotions. The body type analysis unit also uses the emotion estimation function to analyze the emotional responses of a purchaser when trying on clothes in real time. For example, it analyzes the purchaser's facial expressions and voice and calculates an emotion score. The body type analysis unit also builds a system that continuously updates preference trends based on the purchaser's emotional response data. For example, it reevaluates preference trends every time the purchaser's emotions change. In this way, the emotion estimation function can be used to more accurately estimate the purchaser's preference trends.

[0056] The preference analysis unit can collect feedback from multiple people and analyze it comprehensively in order to incorporate the opinions of family or friends. For example, the preference analysis unit provides a function that allows a purchaser to ask family and friends for their opinions on clothes they plan to purchase. For example, it makes it easy to collect feedback within the application. The preference analysis unit also analyzes the feedback from family and friends and comprehensively evaluates the purchaser's preference trends. For example, it integrates the opinions of multiple people to make optimal suggestions. The preference analysis unit also analyzes the emotional reactions of purchasers when selecting clothes with family and friends and estimates preference trends based on that data. For example, it places emphasis on opinions in which the purchaser expresses positive emotions. This makes it possible to analyze preferences more comprehensively by incorporating the opinions of family and friends.

[0057] The preference analysis unit can add a function that dynamically changes according to the season or event, making it possible to make optimal suggestions at any given time. The preference analysis unit, for example, suggests the most suitable clothing for the season or event based on the purchaser's body type and preference information. For example, it suggests clothing made of cool materials in the summer and clothing made of warm materials in the winter. Furthermore, when a purchaser is attending a specific event, the preference analysis unit suggests clothing suitable for that event. For example, it suggests formal clothing suitable for weddings and parties. Furthermore, the preference analysis unit builds a system that dynamically updates the purchaser's preference trends according to the season or event. For example, it can handle cases where preferred styles change with the seasons. This makes it possible to make optimal suggestions according to the season or event.

[0058] The preference analysis unit uses the emotion estimation function to analyze the emotional state of a purchaser when selecting clothes in real time and make suggestions based on that emotion. The preference analysis unit, for example, analyzes the emotional state of a purchaser when selecting clothes in real time and makes optimal suggestions based on that data. For example, it prioritizes suggestions for items for which the purchaser expressed positive emotions. The preference analysis unit also uses the emotion estimation function to monitor changes in the purchaser's emotions when selecting clothes and makes suggestions based on those changes. For example, it suggests different items if the purchaser expressed negative emotions. The preference analysis unit also builds a system that dynamically adjusts the content of suggestions based on the purchaser's emotional state. For example, it updates the content of suggestions every time the purchaser's emotions change. This makes it possible to make suggestions based on the purchaser's emotional state.

[0059] The suggestion unit can suggest optimal clothing by taking into consideration the lifestyle or activities of the purchaser. The suggestion unit, for example, suggests optimal clothing based on the lifestyle and activities of the purchaser. For example, it suggests sportswear that is easy to move in to a purchaser with an active lifestyle. The suggestion unit also analyzes the activities that the purchaser engages in daily and suggests clothing suitable for those activities. For example, it suggests business casual clothing to a purchaser who does a lot of office work. The suggestion unit also suggests optimal clothing according to the season and weather based on the lifestyle and activities of the purchaser. For example, it suggests waterproof clothing to a purchaser who does a lot of outdoor activities. This makes it possible to suggest optimal clothing according to the lifestyle and activities of the purchaser.

[0060] The suggestion unit can analyze the purchaser's personal color, such as skin color, hair color, or eye color, and suggest clothing in a color that suits them best. For example, the suggestion unit analyzes the purchaser's skin color, hair color, or eye color and suggests clothing in a color that suits them best. For example, it suggests clothing in a tone that matches their skin color. The suggestion unit also performs a personal color diagnosis and suggests clothing in a color that suits the purchaser best. For example, it classifies suggestions into spring type, summer type, autumn type, and winter type. The suggestion unit also suggests clothing in a color that suits the season or event based on the purchaser's personal color. For example, it suggests clothing in a color that suits a specific season. This makes it possible to suggest optimal clothing based on the purchaser's personal color.

[0061] The suggestion unit can use the emotion estimation function to analyze the buyer's emotional response to the proposed clothing and make suggestions that elicit positive emotions. For example, the suggestion unit analyzes the buyer's emotional response to the proposed clothing and makes suggestions that elicit positive emotions based on that data. For example, it prioritizes suggesting items for which the buyer expressed positive emotions. The suggestion unit also uses the emotion estimation function to monitor changes in the buyer's emotions toward the proposed clothing and makes suggestions based on those changes. For example, if the buyer expressed negative emotions, it suggests a different item. The suggestion unit also builds a system that dynamically adjusts the content of suggestions based on the buyer's emotional response data. For example, it updates the content of suggestions every time the buyer's emotions change. This makes it possible to suggest optimal clothing based on the buyer's emotional responses.

[0062] The suggestion unit can take into consideration the hobbies or interests of the purchaser and suggest clothing with related designs and brands. For example, the suggestion unit suggests clothing with related designs and brands based on the hobbies and interests of the purchaser. For example, for a purchaser whose hobby is the outdoors, it suggests clothing with outdoor brands. The suggestion unit also analyzes themes and trends in which the purchaser is interested and suggests clothing with designs and brands related to those themes. For example, for a purchaser who is interested in eco-friendly themes, it suggests clothing with sustainable brands. The suggestion unit also suggests clothing with optimal designs and brands according to the season or event based on the hobbies and interests of the purchaser. For example, for a purchaser who is attending a music festival, it suggests clothing with designs and brands suitable for festivals. This makes it possible to suggest optimal clothing based on the hobbies and interests of the purchaser.

[0063] The suggestion unit can also consider the preferences of the purchaser's friends or family and suggest coordination that can be enjoyed by multiple people. The suggestion unit, for example, suggests coordination that can be enjoyed by multiple people based on the preferences of the purchaser's friends and family. For example, it suggests clothes with matching designs and colors for the whole family. Furthermore, when the purchaser attends an event with friends or family, the suggestion unit suggests coordination that is suitable for the event. For example, it suggests coordination that is suitable for a party to be attended with friends. Furthermore, the suggestion unit suggests optimal coordination according to the season or event based on the preferences of the purchaser's friends and family. For example, it suggests coordination that is suitable for a family trip. This makes it possible to suggest coordination that takes into account the preferences of the purchaser's friends and family.

[0064] The suggestion unit can use the emotion estimation function to analyze the buyer's emotional response to the proposed clothing in real time and provide feedback according to that emotion. For example, the suggestion unit analyzes the buyer's emotional response to the proposed clothing in real time and provides feedback based on that data. For example, it prioritizes suggesting items for which the buyer expressed positive emotions. The suggestion unit also uses the emotion estimation function to monitor changes in the buyer's emotions regarding the proposed clothing and provides feedback according to those changes. For example, if the buyer expressed negative emotions, it suggests a different item. The suggestion unit also builds a system that dynamically adjusts the feedback content based on the buyer's emotional response data. For example, it updates the feedback content every time the buyer's emotions change. This makes it possible to provide feedback based on the buyer's emotional response.

[0065] The simulation unit reflects the purchaser's movements or posture in real time, enabling a more realistic simulation of the wearing sensation. For example, the simulation unit captures the purchaser's movements and posture in real time and simulates the wearing sensation of the clothing based on that data. For example, it reflects the purchaser's walking and sitting movements. The simulation unit also allows the purchaser to capture their own movements using a smartphone camera and upload the data to an application. For example, detailed movement data is obtained by taking a picture of the entire body while rotating. The simulation unit also builds a system that updates the clothing simulation in real time based on the purchaser's movements and posture. For example, the clothing simulation changes every time the purchaser moves. This allows a more realistic simulation of the wearing sensation to be achieved by reflecting the purchaser's movements and posture in real time.

[0066] The simulation unit can add a function that allows a purchaser to set different lighting conditions or backgrounds and check how the clothing will feel when worn in various situations. For example, the simulation unit provides a function that allows a purchaser to select different lighting conditions or backgrounds and simulates the clothing according to the settings. For example, the purchaser can select indoor or outdoor, daytime or nighttime situations. The simulation unit also simulates how the color and texture of the clothing will look by changing the lighting conditions and background. For example, the simulation unit compares how the clothing will look under different lighting conditions. The simulation unit also allows a purchaser to set a situation that matches a specific event or location and simulate how the clothing will feel when worn in that situation. For example, the purchaser can set a background of a beach or a party. This allows the purchaser to check how the clothing will feel when worn in various situations by setting different lighting conditions and backgrounds.

[0067] The simulation unit can use the emotion estimation function to analyze the emotional reactions of a purchaser when viewing a simulation and provide a simulation that elicits positive emotions. For example, the simulation unit analyzes the emotional reactions of a purchaser when viewing a simulation and provides a simulation that elicits positive emotions based on that data. For example, it preferentially displays simulations in which the purchaser expressed positive emotions. The simulation unit also uses the emotion estimation function to monitor changes in the purchaser's emotions when viewing a simulation and provides a simulation based on those changes. For example, if the purchaser expressed negative emotions, it provides a different simulation. The simulation unit also builds a system that dynamically adjusts the content of the simulation based on the purchaser's emotional reaction data. For example, it updates the content of the simulation every time the purchaser's emotions change. This makes it possible to provide a positive simulation based on the purchaser's emotional reactions.

[0068] The simulation unit can also feature avatars of the purchaser's friends or family members to simulate outfits for multiple people. The simulation unit provides, for example, a function for creating avatars of the purchaser's friends and family members and having them appear in the simulation. For example, the simulation unit simulates outfits of matching designs and colors for the whole family. When the purchaser attends an event with friends or family members, the simulation unit simulates outfits suitable for the event. For example, the simulation unit simulates outfits suitable for a party attended with friends. The simulation unit also simulates optimal outfits according to the season or event, based on the avatars of the purchaser's friends and family members. For example, the simulation unit simulates outfits suitable for a family trip. This makes it possible to simulate outfits for multiple people using avatars of the purchaser's friends and family members.

[0069] The simulation unit can add a function to display other users' ratings or comments on the clothes selected by the purchaser in real time. The simulation unit provides, for example, a function to display other users' ratings and comments on the clothes selected by the purchaser in real time. For example, it displays the "likes" and comments that other users have made. The simulation unit also displays an overall rating of the clothes selected by the purchaser based on other users' ratings and comments. For example, it prioritizes displaying items with many positive ratings. The simulation unit also analyzes other users' feedback on the clothes selected by the purchaser and makes optimal suggestions based on that data. For example, it suggests items that other users have highly rated. In this way, by displaying other users' ratings and comments in real time, the simulation unit supports the purchaser's selection.

[0070] The simulation unit can use the emotion estimation function to analyze the emotional reactions of a buyer when viewing a simulation in real time and provide feedback according to those emotions. For example, the simulation unit analyzes the emotional reactions of a buyer when viewing a simulation in real time and provides feedback based on that data. For example, it preferentially displays simulations in which the buyer expressed positive emotions. The simulation unit also uses the emotion estimation function to monitor changes in the buyer's emotions when viewing a simulation and provides feedback according to those changes. For example, if the buyer expressed negative emotions, it provides a different simulation. The simulation unit also builds a system that dynamically adjusts the feedback content based on the buyer's emotional reaction data. For example, it updates the feedback content every time the buyer's emotions change. This makes it possible to provide feedback based on the buyer's emotional reactions.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The suggestion unit can take into consideration the hobbies and interests of the purchaser and suggest clothing with related designs and brands. For example, for a purchaser whose hobby is the outdoors, it will suggest clothing from outdoor brands. The suggestion unit can also analyze themes and trends that the purchaser is interested in and suggest clothing with designs and brands related to those themes. For example, for a purchaser who is interested in eco-friendly themes, it will suggest clothing from sustainable brands. The suggestion unit can also suggest clothing with optimal designs and brands according to the season or event based on the purchaser's hobbies and interests. For example, for a purchaser attending a music festival, it will suggest clothing with designs and brands suited to festivals. This makes it possible to suggest optimal clothing based on the purchaser's hobbies and interests.

[0073] The simulation unit reflects the purchaser's movements and posture in real time, enabling a more realistic simulation of how the garment will feel when worn. For example, it reflects the purchaser's walking and sitting movements. The simulation unit also allows the purchaser to capture their own movements using their smartphone camera and upload the data to the application. For example, detailed movement data can be obtained by taking a picture of the entire body while rotating. The simulation unit also builds a system that updates the clothing simulation in real time based on the purchaser's movements and posture. For example, the clothing simulation changes every time the purchaser moves. This allows a more realistic simulation of how the garment will feel when worn by reflecting the purchaser's movements and posture in real time.

[0074] The suggestion unit can suggest optimal clothing by taking into consideration the lifestyle and activities of the purchaser. For example, it will suggest sportswear that is easy to move in to a purchaser with an active lifestyle. The suggestion unit also analyzes the purchaser's daily activities and suggests clothing suitable for those activities. For example, it will suggest business casual clothing to a purchaser who does a lot of office work. The suggestion unit also suggests optimal clothing according to the season and weather based on the purchaser's lifestyle and activities. For example, it will suggest waterproof clothing to a purchaser who does a lot of outdoor activities. This makes it possible to suggest optimal clothing according to the purchaser's lifestyle and activities.

[0075] The simulation unit can add a function that allows a purchaser to set different lighting conditions and backgrounds and check how the clothing will feel when worn in various situations. For example, a function that allows a purchaser to select different lighting conditions and backgrounds is provided, and a simulation of the clothing is performed according to the settings. For example, the purchaser can select indoor or outdoor, daytime or nighttime situations. The simulation unit can also simulate how the color and texture of the clothing will look by changing the lighting conditions and background. For example, the simulation unit can compare how the clothing will look under different lighting conditions. The simulation unit can also set a situation that matches a specific event or location, and simulate how the clothing will feel when worn in that situation. For example, the purchaser can set a background of a beach or a party. This allows the purchaser to check how the clothing will feel when worn in various situations by setting different lighting conditions and backgrounds.

[0076] The suggestion unit can analyze the personal colors of the purchaser, such as skin color, hair color, and eye color, and suggest clothing in the most suitable shade. For example, it can analyze the skin color, hair color, and eye color of the purchaser and suggest clothing in the most suitable shade. For example, it can suggest clothing in a tone that matches the skin color. The suggestion unit also performs a personal color diagnosis and suggests clothing in the most suitable shade for the purchaser. For example, it can classify and suggest clothing into spring type, summer type, autumn type, and winter type. The suggestion unit can also suggest clothing in the most suitable shade for a season or event based on the purchaser's personal color. For example, it can suggest clothing in a shade that suits a specific season. This makes it possible to suggest the most suitable clothing based on the purchaser's personal color.

[0077] The suggestion unit can use the emotion estimation function to analyze the buyer's emotional response to the proposed clothing and make suggestions that elicit positive emotions. For example, the suggestion unit can analyze the buyer's emotional response to the proposed clothing and make suggestions that elicit positive emotions based on that data. For example, the suggestion unit can prioritize items for which the buyer expressed positive emotions. The suggestion unit can also use the emotion estimation function to monitor changes in the buyer's emotions toward the proposed clothing and make suggestions based on those changes. For example, if the buyer expressed negative emotions, the suggestion unit can suggest a different item. The suggestion unit can also build a system that dynamically adjusts the content of suggestions based on the buyer's emotional response data. For example, the content of suggestions can be updated every time the buyer's emotions change. This makes it possible to suggest optimal clothing based on the buyer's emotional responses.

[0078] The simulation unit can use the emotion estimation function to analyze the emotional reactions of a purchaser when viewing a simulation and provide a simulation that elicits positive emotions. For example, it can analyze the emotional reactions of a purchaser when viewing a simulation and provide a simulation that elicits positive emotions based on that data. For example, it can preferentially display simulations in which the purchaser expressed positive emotions. The simulation unit also uses the emotion estimation function to monitor changes in the purchaser's emotions when viewing a simulation and provide a simulation based on those changes. For example, it can provide a different simulation if the purchaser expressed negative emotions. The simulation unit also builds a system that dynamically adjusts the content of the simulation based on the purchaser's emotional reaction data. For example, it can update the content of the simulation every time the purchaser's emotions change. This makes it possible to provide a positive simulation based on the purchaser's emotional reactions.

[0079] The suggestion unit can use the emotion estimation function to analyze the emotional state of a purchaser when selecting clothes in real time and make suggestions based on that emotion. For example, the suggestion unit can analyze the emotional state of a purchaser when selecting clothes in real time and make optimal suggestions based on that data. For example, it can prioritize suggestions for items for which the purchaser expressed positive emotions. The suggestion unit can also use the emotion estimation function to monitor changes in the purchaser's emotions when selecting clothes and make suggestions based on those changes. For example, it can suggest different items if the purchaser expressed negative emotions. The suggestion unit can also build a system that dynamically adjusts the content of suggestions based on the purchaser's emotional state. For example, it can update the content of suggestions every time the purchaser's emotions change. This makes it possible to make suggestions based on the purchaser's emotional state.

[0080] The simulation unit can use the emotion estimation function to analyze the emotional reactions of a buyer when viewing a simulation in real time and provide feedback according to those emotions. For example, the simulation unit can analyze the emotional reactions of a buyer when viewing a simulation in real time and provide feedback based on that data. For example, it can preferentially display simulations in which the buyer expressed positive emotions. The simulation unit can also use the emotion estimation function to monitor changes in the buyer's emotions when viewing a simulation and provide feedback according to those changes. For example, it can provide a different simulation if the buyer expressed negative emotions. The simulation unit can also build a system that dynamically adjusts the content of the feedback based on the buyer's emotional reaction data. For example, it can update the content of the feedback each time the buyer's emotions change. This makes it possible to provide feedback based on the buyer's emotional reactions.

[0081] The suggestion unit can also consider the preferences of the purchaser's friends and family to suggest outfits that can be enjoyed by multiple people. For example, it suggests outfits that can be enjoyed by multiple people based on the preferences of the purchaser's friends and family. For example, it suggests clothes with matching designs and colors for the whole family. Furthermore, when the purchaser attends an event with friends or family, the suggestion unit suggests outfits that are suitable for the event. For example, it suggests outfits that are suitable for a party to be attended with friends. Furthermore, the suggestion unit suggests optimal outfits according to the season or event based on the preferences of the purchaser's friends and family. For example, it suggests outfits that are suitable for a family trip. This makes it possible to suggest outfits that take into account the preferences of the purchaser's friends and family.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The body type analysis unit analyzes the buyer's body type information. For example, it analyzes body type based on information entered by the buyer, such as height, weight, and body fat percentage. It can also perform a detailed body type analysis using the buyer's 3D scan data. It can also analyze the buyer's body type information in combination with past purchase history and social media posts. Step 2: The preference analysis unit analyzes the buyer's preferences. For example, it analyzes based on the buyer's input preferences for color, style, and brand. It can also infer preferences by analyzing the buyer's past purchase history and social media posts. It can also collect feedback from multiple people to incorporate the opinions of family and friends and perform a comprehensive analysis. Step 3: The suggestion unit suggests the most suitable clothing based on the analysis results of the body type analysis unit and preference analysis unit. For example, it searches a database for clothing that suits the purchaser's body type and preferences and suggests the best clothing. It can also suggest the most suitable clothing taking into account the purchaser's lifestyle and activities. It can also analyze the purchaser's personal colors, such as skin color, hair color, and eye color, and suggest clothing in the most suitable shades. Step 4: The simulation unit simulates wearing the clothing proposed by the proposal unit. For example, it uses a 3D model to simulate the impression a purchaser will have when wearing the proposed clothing, generating images and videos that can be visually confirmed. It can also reflect the purchaser's movements and posture in real time to simulate a more realistic wearing experience. It can also provide a function that allows users to set different lighting conditions and backgrounds to check how the clothing will feel in various situations.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0143] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a body type analysis unit that analyzes body type information of the purchaser; a preference analysis unit that analyzes the preferences of the purchaser; a suggestion unit that suggests optimal clothing based on the analysis results of the body type analysis unit and the preference analysis unit; a simulation unit that performs a simulation of wearing the clothing proposed by the proposal unit. A system characterized by:

2. The body type analysis unit Analyzing a customer's past purchase history or social media posts to more accurately estimate their preferences 2. The system of claim 1.

3. The body type analysis unit The buyer's body shape information is acquired using 3D scanning technology, and a detailed body shape analysis is performed.

2. The system of claim 1.

4. The body type analysis unit Analyzing a customer's emotional reactions to clothing they have previously purchased and estimating their preferences 2. The system of claim 1.

5. The preference analysis unit Collect and analyze feedback from multiple people, including family and friends.

2. The system of claim 1.

6. The preference analysis unit Add a function that dynamically changes depending on the season or event, and make the best suggestions at that time.

2. The system of claim 1.

Images